Make Countries Move

knitr::opts_chunk$set(echo = TRUE)

Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

• Submit to Github this script with solutions and its knitted version.
  
• Submit to Brightspace a document with the (1) URL of this script and its knitted version in Github (2) answer of 250 words and a (3) visualisation of the Final Question

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

# install.packages("gganimate")
# install.packages("gifski")
# install.packages("av")
# install.packages("gapminder")
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gganimate)
library(gifski)
library(av)
library(gapminder)

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

highest_gdp_country <- subset(gapminder, year == 1952)[which.max(subset(gapminder, year == 1952)$gdpPercap), ]
highest_gdp_country

## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.

gdp_country_2007 <- subset(gapminder, year==2007)

gdp_2007_sorted <- gdp_country_2007[order(-gdp_country_2007$gdpPercap), ]

Five_richest_countries <- head(gdp_2007_sorted, 5)

Five_richest_countries

## # A tibble: 5 × 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     2007    80.2   4627926    49357.
## 2 Kuwait        Asia       2007    77.6   2505559    47307.
## 3 Singapore     Asia       2007    80.0   4553009    47143.
## 4 United States Americas   2007    78.2 301139947    42952.
## 5 Ireland       Europe     2007    78.9   4109086    40676.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() +
  ggtitle("Figure 01")+
  xlab("GDP per Citizen")+
  ylab("Life Expectancy")

  options(scipen = 999)

…

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() +
  ggtitle("Figure 02")+ 
  xlab("GDP per Citizen") +
  ylab("Life Expectancy") 

options(scipen = 999)

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Questions for the static figures:

1. Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result)

• scale_x_log10 is important for the x axes, to make the data more visible to see and analyse

2. Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis?

• Kuwait

3. Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)

• Done
• I used: aes(color=continent) in geom_point to make the continents show

4. Answer: What are the five richest countries in the world in 2007? The five riches countries in 2007 is: Norway, Kuwait, Singapore, United States and Ireland

I used the codes:

gdp_country_2007 <- subset(gapminder, year==2007) #To filter the year 2007

gdp_2007_sorted <- gdp_country_2007[order(-gdp_country_2007$gdpPercap), ]

Five_richest_countries <- head(gdp_2007_sorted, 5) #To filter in the five riches countries

Five_richest_countries

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color=continent)) +
  scale_x_log10()  # convert x to log scale
anim +
  xlab("GDP per Citizen") +
  ylab("Life Expectancy") 

options(scipen = 999)

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1) +
  xlab("GDP per Citizen") +
  ylab("Life Expectancy") 

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop,)) +
  geom_point(aes(color=continent)) +
  scale_x_log10() + 
  transition_time(year)+
  labs(title="Year: {frame_time}")+
  xlab("GDP per Citizen")+
  ylab("Life Expextancy")
anim2

options(scipen = 999)

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Tasks for the animations:

5. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

• I used the labs(title=“Year: {frame_time}”) to make the title “year” with a time frame that shows the developement of time

6. Can you make the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color

• To change the langes of the x and y axes i used the code: xlab(“GDP per Citizen”)+ ylab(“Life Expextancy”)
  
• To highlight the continents in different colers i used the code: geom_point(aes(color=continent))

Final Question

7. Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]

When defining whether the world is a better place today than in the year we were born, we use the year 2002. We define “better” by gdp per capita, which is defined as: “Average economic output per person in a country or region per year”. This indicates the wealth of the citizens in each country.The newest data from gapminder is from 2007, and we will therefore compare 2002 to 2007. We are aware that the information of 2007 is outdated in comparison to 2025, but due to trouble with uploading the newest data we have to work with what we have.

When we look at the comparison between 2002 and 2007 we can see minor improvements for all continents with similar rise in GDP. The animation confirms this statement when showing the movement towards a higher GPR per. capita. You can see from both the graph and animation that Europe and Oceania have a significally higher GDP per capita than Africa both in 2002 and 2007. So there is a general incline for all but the division between Europa, Oceania and Africa remains roughly the same. So is this world gotten any better in five years? A bit, but not the big improvements have been made.

However, our analysis is lacking in terms of it missing out on the general inflation in for example house prices and so on because of the worldwide financial crises we saw in 2007/2008.

library(ggplot2)
library(dplyr)

# Filter dataset for 2002 and 2007, then summarize by continent
gapminder_filtered <- gapminder %>%
  filter(year %in% c(2002, 2007)) %>%
  group_by(continent, year) %>%
  summarise(avg_gdpPercap = mean(gdpPercap), .groups = 'drop')

# Create block diagram (bar chart)
ggplot(gapminder_filtered, aes(x = continent, y = avg_gdpPercap, fill = continent)) +
  geom_col(position = "dodge") +  # Creates grouped bars for 2002 and 2007
  facet_wrap(~year) +  # Separate plots for 2002 and 2007
  scale_y_log10() +  # Log scale for better visualization
  labs(
    title = "Average GDP per Capita by Continent (2002 vs 2007)",
    x = "Continent",
    y = "Average GDP per Capita (log scale)",
    fill = "Continent"
  ) +
  theme_minimal()